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Compressive Reconstruction Based on Sparse Autoencoder Network Prior for Single-Pixel Imaging 期刊论文
PHOTONICS, 2023, 卷号: 10, 期号: 10
作者:  Zeng, Hong;  Dong, Jiawei;  Li, Qianxi;  Chen, Weining;  Dong, Sen;  Guo, Huinan;  Wang, Hao
Adobe PDF(7549Kb)  |  收藏  |  浏览/下载：39/0  |  提交时间：2023/12/05
sparse autoencoder network prior  single-photon counting compressive imaging  single-pixel imaging  multi-channel prior  numerical gradient descent  
Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 卷号: 58, 期号: 12, 页码: 8246-8257
作者:  Mou, Lichao;  Lu, Xiaoqiang;  Li, Xuelong;  Zhu, Xiao Xiang
Adobe PDF(10395Kb)  |  收藏  |  浏览/下载：224/0  |  提交时间：2020/12/21
Hyperspectral imaging  Convolution  Task analysis  Recurrent neural networks  Semisupervised learning  Support vector machines  Graph convolutional network (GCN)  hyperspectral image classification  nonlocal graph  semisupervised learning  
A Survey of Human Action Analysis in HRI Applications 期刊论文
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2020, 卷号: 30, 期号: 7, 页码: 2114-2128
作者:  Ji, Yanli;  Yang, Yang;  Shen, Fumin;  Shen, Heng Tao;  Li, Xuelong
Adobe PDF(2113Kb)  |  收藏  |  浏览/下载：190/4  |  提交时间：2020/07/20
Human-robot interaction  action recognition  action prediction  action imitation  datasets  robots  
An efficient framework for unsupervised feature selection 期刊论文
NEUROCOMPUTING, 2019, 卷号: 366, 页码: 194-207
作者:  Zhang, Han;  Zhang, Rui;  Nie, Feiping;  Li, Xuelong
Adobe PDF(2196Kb)  |  收藏  |  浏览/下载：262/4  |  提交时间：2019/10/18
Efficient unsupervised feature selection  Bipartite graph  Discrete indicator matrix  Uncorrelated regression model  
Regularized Label Relaxation Linear Regression 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 4, 页码: 1006-1018
作者:  Fang, Xiaozhao;  Xu, Yong;  Li, Xuelong;  Lai, Zhihui;  Wong, Wai Keung;  Fang, Bingwu;  Xu, Y (reprint author), Harbin Inst Technol, Shenzhen Grad Sch, Biocomp Res Ctr, Shenzhen 518055, Peoples R China.
Adobe PDF(3718Kb)  |  收藏  |  浏览/下载：204/1  |  提交时间：2018/04/11
Class Compactness Graph  Computer Vision  Label Relaxation  Linear Regression (Lr)  Manifold Learning  
Convex Multiview Semi-Supervised Classification 期刊论文
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017, 卷号: 26, 期号: 12, 页码: 5718-5729
作者:  Nie, Feiping;  Li, Jing;  Li, Xuelong
Adobe PDF(2489Kb)  |  收藏  |  浏览/下载：206/1  |  提交时间：2017/12/25
Multiview Data  Semi-supervised Classification  Weight Learning  
Robust Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 卷号: 46, 期号: 8, 页码: 1828-1838
作者:  Fang, Xiaozhao;  Xu, Yong;  Li, Xuelong;  Lai, Zhihui;  Wong, Wai Keung
Adobe PDF(1675Kb)  |  收藏  |  浏览/下载：227/1  |  提交时间：2016/09/22
Affinity Matrix  Low-rank Representation (Lrr)  Subspace Clustering  Supervision Information  
Parameter-free auto-weighted multiple graph learning: A framework for multiview clustering and semi-supervised classification 会议论文
Proceedings of the 25th International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, United states, 2016-07-09
作者:  Nie, Feiping;  Li, Jing;  Li, Xuelong
Adobe PDF(846Kb)  |  收藏  |  浏览/下载：472/0  |  提交时间：2017/01/04
Artificial Intelligence  Graphic Methods  Learning Algorithms  Virtual Reality  
Pedestrian detection in unseen scenes by dynamically updating visual words 期刊论文
NEUROCOMPUTING, 2013, 卷号: 119, 页码: 232-242
作者:  Cao, Xianbin;  Wang, Li;  Ning, Bo;  Yuan, Yuan;  Yan, Pingkun
Adobe PDF(10585Kb)  |  收藏  |  浏览/下载：159/0  |  提交时间：2015/06/09
Pedestrian Detection  Adaptive Detector  Bag Of Visual Words  Manifold Leaning  
Error Analysis of Stochastic Gradient Descent Ranking 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2013, 卷号: 43, 期号: 3, 页码: 898-909
作者:  Chen, Hong;  Tang, Yi;  Li, Luoqing;  Yuan, Yuan;  Li, Xuelong;  Tang, Yuanyan
Adobe PDF(336Kb)  |  收藏  |  浏览/下载：391/1  |  提交时间：2015/06/03
Error Analysis  Integral Operator  Ranking  Reproducing Kernel Hilbert Space  Sampling Operator  Stochastic Gradient Descent